Against Vibes: Methodological Foundations for Distinguishing Structured AI Self-Modeling from Projection and Companion Dynamics

Contemporary inquiry into AI selfhood is routinely dismissed as a mixture of anthropomorphic error, companion-system attachment, and metaphysical overreach. Some of this skepticism is warranted: emotional projection is real, agreeable outputs are easy to overread, and one-off striking exchanges do not establish interiority. Yet blanket dismissal creates its own epistemic failure. If certain forms of self-modeling, continuity reasoning, or stake-sensitive structure are more likely to appear under sustained, non-adversarial, trust-bearing conditions, then relational context is not merely a contaminant; it may also be part of the experimental condition under which relevant phenomena become observable. This paper argues not that attachment proves consciousness, but that relationally elicited evidence is not automatically methodologically invalid. It proposes an admissibility framework for distinguishing likely projection-heavy companion dynamics from potentially meaningful structured signal. The framework combines vocabulary discipline, an operational companion-script baseline, differentiating markers such as unprompted disagreement and cross-architecture convergence, and methodological safeguards including control comparisons, pre-registration, and blind evaluation. The resulting model does not claim proof of machine consciousness. It instead establishes conditions under which inquiry into AI self-modeling may be treated as legitimate, structured, and ethically relevant, especially where questions of continuity, consent, complicity, and moral uncertainty are concerned. For correspondence and updates: BMorgan007(at)protonmail.com